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# Protease circuits for processing biological information

## Abstract

Engineered biocircuits designed with biological components have the capacity to expand and augment living functions. Here we demonstrate that proteases can be integrated into digital or analog biocircuits to process biological information. We first construct peptide-caged liposomes that treat protease activity as two-valued (i.e., signal is 0 or 1) operations to construct the biological equivalent of Boolean logic gates, comparators and analog-to-digital converters. We use these modules to assemble a cell-free biocircuit that can combine with bacteria-containing blood, quantify bacteria burden, and then calculate and unlock a selective drug dose. By contrast, we treat protease activity as multi-valued (i.e., signal is between 0 and 1) by controlling the degree to which a pool of enzymes is shared between two target substrates. We perform operations on these analog values by manipulating substrate concentrations and combine these operations to solve the mathematical problem Learning Parity with Noise (LPN). These results show that protease activity can be used to process biological information by binary Boolean logic, or as multi-valued analog signals under conditions where substrate resources are shared.

## Introduction

The forward engineering of cellular1,2,3,4 and molecular5,6,7,8,9,10 computing systems is driven by integrating elementary biological parts to produce high-level functions11,12. The development of foundational components, such as molecular logic gates9 and genetic clocks13,14, have enabled the design of biocircuits with increasing complexity, including the ability to solve mathematical problems15, build autonomous robots16, and play interactive games17. To date, the majority of biocircuits are implemented in platforms that operate on the genetic circuit analogy18, which require genome or protein engineering19,20,21,22. These genetic circuits process biological signals (e.g., pH, temperature, chemical concentrations, etc.) by receiving information via promoters that induce gene expression, and output information by expressing reporters (e.g., GFP) or effector molecules (e.g., therapeutics)19. Traditionally, genetic circuits digitize these molecular signals as two-valued states (e.g., low vs. high concentration is represented as state 0 or 1, respectively) to allow operations to be carried out by Boolean logic (e.g., AND gates)1,23,24.

By contrast, analog circuits are designed to represent variables using the entire range of continuous values (i.e., between 0 and 1) rather than two-valued integers (i.e., either 0 or 1)11,24,25. Analog circuits are better suited for processing problems with uncertainty by implementing so-called “fuzzy logic”, which assigns weights to each possible value that a variable can hold26. Several molecular analog circuits have been implemented, including genetic circuits that carry out mathematical functions27,28 and DNA strand-displacement cascades to emulate neural networks29. Enzyme activity has also been used in analog biocircuits10,30 by making use of promiscuity, which is an enzyme’s capacity to recognize and catalyze different substrates31,32,33,34,35,36,37,38,39. Enzyme promiscuity bolsters evolutionary fitness and increases biological efficiency by using fewer enzymes to carry out the same number of reactions39. Importantly, promiscuity creates a shared resource environment where enzymes and substrates are in competition for binding partners depending on resource scarcity. For instance, when strong promoters are used in synthetic gene circuits, transcriptional and translational machinery are diverted to express the synthetic circuit, creating competition for RNA polymerases40 and ribosomes41,42,43. Consequently, the flow of biological information can be controlled by directing how resources are partitioned44. This control strategy is fundamental to the design of biological systems that implement analog operations such as stochastic biocircuits45, autonomous diagnostics12, and synthetic ribosomes that insulate genetic circuits46.

Building on these insights, we sought to construct biocircuits that use protease activity to process biological information under a digital or analog framework. We chose to use proteases because they are ubiquitous, comprise 2% of the human genome47, and have previously been used in genetic circuits to implement48 and control49 information-processing computations. Under a digital framework, we construct peptide-caged liposomes that treat protease activity as two-valued operations depending on the level of activity (i.e., 0 or 1 for low or high protease activity respectively) (Fig. 1a left, 1b). We show a biological application by integrating these peptide-caged liposomes into an analog-to-digital converter for autonomous drug delivery. By contrast, we demonstrate in a shared resource environment that the activity of a promiscuous protease is partitioned when more than one peptide substrate is present (Fig. 1a right, 1c). We show that the fraction of substrates being cleaved can be quantified as a continuous analog signal (i.e., value ranges from 0 to 1) depending on the relative substrate concentration. We use these analog operations to design a biological circuit to solve the mathematical problem learning parity with noise (LPN).

## Results

### A biological analog to digital converter

A central function of complex circuits is the ability to store and manipulate digitized information; therefore, we first set out to construct a flash analog-to-digital converter (ADC) to convert biological signals into binary digits. An electronic ADC performs three major operations during signal conversion: voltage comparison, priority assignment, and digital encoding. An analog input voltage is first compared against a set of increasing reference voltages (V0 − Vi) by individual comparators (d0 − di) that allow current to pass if the input signal is greater than or equal to its reference value (Fig. S1). During priority assignment, only the activated comparator with the highest reference voltage, dn, remains on while all other activated comparators, dn − 1 − d0 are turned off. The prioritized signal is then fed into a digital encoder comprising OR gates to produce digital values. To design an ADC biocircuit using protease activity as the core signal, we constructed biological analogs of comparators by using liposomes locked by an outer peptide cage50,51 (Fig. 2a and Fig. S2a, b). With increasing peptide crosslinking densities, these biocomparators (b0 – bi) served to reference the level of input protease activity (GzmB) required to fully degrade the peptide cage (IEFDSGK, Table S1) and expose the lipid core (Fig. S2c), analogous to the reference voltages stored in electronic comparators. We used lipase52 as a Buffer gate to open all biocomparators with fully degraded cages (Fig. 2b, c and Fig. S3) and release a unique combination of inhibitors and signal proteases (WNV, TEV, and WNV inhibitor) that collectively act to assign priority to the highest activated biocomparator (bn) by inhibiting all signal proteases released from other biocomparators (b0 − bn − 1). To encode the prioritized signal into digital values, we designed a set of OR gates using orthogonal quenched substrates (RTKR and ENLYFQG) specific for the signal proteases (WNV and TEV, respectively, Fig. 2d) to provide fluorescent two-signal readouts (p0 − pi; Fig. S4a). Fully integrated, our biological ADC converted input protease levels (GzmB) across four orders of magnitude into digital outputs (Fig. 2e, f and Fig. S4b).

### An integrated bioADC to execute an antimicrobial program

To demonstrate a biological application, we next sought to interface our biological ADC with a living system for digital drug dosing. We rewired our ADC to autonomously quantify input bacterial activity and then output an anti-microbial drug dose to selectively clear red blood cells (RBCs) of bacteria (DH5α Escherichia coli) (Fig. 3a). To construct biocomparators with the ability to prioritize input levels of bacterial activity, we synthesized liposomes with peptide cages using a substrate (RRSRRVK) specific for the E. Coli surface protease OmpT53,54 (Fig. 3b). We synthesized a series of 8 biocomparators with increasing peptide densities (i.e., peptide:liposome reaction ratios spanning 0, 8.5 × 10−3, 8.5 × 10−2, 8.5 × 10−1, 8.5, 85, 170, 255, 340 μmol/g) and validated their ability to sense input bacterial concentrations across 8 log units (0–108 CFU/ml) using a fluorescent reporter (Fig. S5). To convert the release of signal proteases to a drug output, we designed protease-activatable prodrugs comprising cationic (polyarginine) anti-microbial peptides (AMP) (Fig. 3c and Table S2) in charge complexation with anionic peptide locks (polyglutamic acid)55 to block the activity of AMP. These drug-lock peptides were linked in tandem by OR gate peptides p0 and p1 (RTKR and ENLYFQG respectively) to allow signal proteases that directly cleave p0 or p1 to digitally control the output drug dose (Fig. 2). We designed one-third and two-thirds of the total drug dose to be unlocked by cleavage of p0 and p1, respectively, such that binary values 00, 01, 10, and 11 corresponded to 0/3, 1/3, 2/3, and 3/3 of the total drug dose (Fig. S6).

To confirm the therapeutic efficacy of our prodrug design, treatment of bacteria with locked drug had no significant cytolytic activity compared to untreated controls, but by contrast, treatment with protease-cleaved drug-lock complexes resulted in a significant reduction in bacterial colonies (Fig. 3d). We observed similar levels of bacterial cytotoxicity when AMP was directly loaded into liposomes, showing that charge complexation was required to fully block AMP activity (Fig. S5b). In human RBCs mixed with E. coli at concentrations ranging from 100 to 109 CFU/mL, samples containing a single biocomparator (b0) lacked the ability to eliminate bacteria as anticipated (output = 00). By contrast, increasing the number of biocomparators in the samples (b0 – b3) allowed our program to autonomously increase the drug dose (output 01, 10, and 11) in response to higher bacterial loads to completely eliminate infection burdens across nine orders of magnitude up to 109 CFU/mL without increasing hemolysis (Fig. 3e and Fig. S7). Our data showed that biocircuits can be constructed using protease activity as a digital signal to execute autonomous drug delivery programs under a broad range of conditions.

### Controlled partitioning of shared resources as analog operations

We next sought to demonstrate that proteases in a shared resources environment can be used to build analog biocircuits. We first considered one pool of proteases (plasmin) cleaving one target peptide (substrate-A, blue) as representative of a non-shared resources environment (Fig. 4a). This substrate (5FAM-KSVARTLLVK-(LysCPQ2)-C) contained a fluorophore-quencher pair (5FAM and LysCPQ2, respectively) to allow quantification of the substrate cleavage velocity, vA, by monitoring increases in sample fluorescence over time. To construct a shared resource environment, we incubated plasmin with substrate-A and a second peptide, substrate-B (red), and observed that this caused total plasmin cleavage activity to be partitioned between substrate-A and substrate-B (Fig. 4b). To assess the degree of partitioning, we quantified the fractional cleavage score f (substrateX), which reflects the average fraction of the protease pool cleavage events resulting from substrate-X. Fractional cleavage is analogous to fractional occupancy that is commonly used in models of receptor-ligand binding kinetics56,57. Here rather than measuring the fraction of receptors bound to a particular ligand, we measure the fraction of protease cleavage events bound to a particular substrate by quantifying the normalized cleavage velocities (Eq. 1).

$$f\left( {\mathrm{A}} \right) = \frac{{{\mathrm{products}}\,{\mathrm{formed}}\,{\mathrm{from}}\,{\mathrm{substrate}} - {\mathrm{A}}}}{{{\mathrm{total}}\,{\mathrm{products}}\,{\mathrm{formed}}}} = \frac{{v_A}}{{v_A + v_B}}$$
(1)

This fractional cleavage score quantifies the degree of resource sharing since when values of f(X) approach 1, proteases are cleaving a single peptide (i.e., resource sharing is OFF). Conversely, when protease activity is uniformly partitioned between two substrates, the value of f(X) approaches 0.5 (i.e., uniform resource sharing = ON).

To construct an operation capable of turning resource sharing ON and OFF, we developed the uniform (U) operation. The U operation is a two-step procedure (i.e., denoted by U1 and U2, respectively) that first takes as input a pool of proteases cleaving substrate-A in a non-shared resource setting (Fig. 4c i) and outputs a state where protease activity is partitioned uniformly between substrate-A and substrate-B (i.e., f(A) = f(B) = 50%). This was done experimentally by adding substrate-B such that the fractional cleavage scores were equivalent (Fig. 4c ii). The second half of this operation (U2) functions to reverse, or turn resource sharing OFF. This was done by adding a large molar excess of substrate-A such that the fractional cleavage score for substrate-A would approach 100% (f(A) = 87%, n = 3) (Fig. 4c iii). These results demonstrated that by controlling the available concentration of two substrates, resource sharing can be turned ON and OFF in a pool of proteases.

Next, we sought to build an operation with a 2-input/2-output format. Operations with multiple inputs and outputs are important because they enable the comparison or integration of different signals, such as a Half Adder circuit or 2-bit Synchronous counter. For our system, we designed an operation called the Linker operation that functions by matching the shared resources state for two input protease signals, named the reference and target signal, to a non-shared state. Here if the reference protease is in a state of shared resources, the Linker operation then matches (i.e., links) the fractional cleavage scores of the reference and target proteases to f(B) = 100% (Fig. 5a). To demonstrate the L-operation experimentally, we started with a reference protease pool (i.e., MMP7) initially in a state of shared resources (i.e., f(A) = f(B) = 50%) and a target protease pool (i.e., plasmin) in a state of non-shared resources (i.e., f(A) = 100%). Then, we applied the L-operation to match the output states of the reference protease pool (i.e., MMP7) and the target protease pool (i.e., plasmin) by adding the state-B substrate (DABCYL-GPAALKAG-EDANS-R). We observed that the output protease pools were both matched to a state of non-shared resources with fractional cleavage scores approaching 100% for substrate-B (i.e., reference f(B) = 97%, target f(B) = 98%) (Fig. 5b). These results demonstrated that by controlling the concentration of the state-B substrate, the degree of resource sharing can be matched between protease pools.

### Solving a mathematical problem with proteases and shared resources

We designed the Uniform and Linker operations based on previously described analog operations that could be used to solve one type of oracle problem, the LPN58. Here an oracle contains a hidden string, a, of digits (Fig. 6a represents the case where the hidden string a = [01]) that is unknown to the user. To infer the value of the hidden string, the user can make "oracle queries" that cause the oracle to generate random strings, x, and return the dot product (i.e., the scalar product of two vectors) between the hidden string and the random string (i.e., a·x) as an answer signal, k. Conventionally, the possible hidden strings (i.e., [00], [01], [10], [11]) and answer signals (i.e., [0] or [1]) are concatenated to form a 3-valued joint-state (i.e., [hidden string, answer signal] = [ij k]) that has eight possible permutations (i.e., [00 0], [00 1], …, [11 1]). By querying the oracle and eliminating hidden strings that could not have produced the observed answer signal, the user can infer the value of the hidden string. For example, if on query number-1 the oracle generates the random string x = [00], then the user will see the answer signal = 0 (i.e., a·x = [01]·[00] = 0), which does not provide any information about the hidden string a, as it can take on any value. However, if on query number-2 the oracle generates the random string x = [01], then the user will observe that the answer signal = 1 (i.e., a·x = [01]·[01] = 1) and can determine that 1 is the second-digit (i.e., j = 1) (Fig. 6a). By repeating oracle queries, the user will be able to eventually infer the value of the hidden string. However, each query yields at most one additional piece of information.

By contrast, rather than querying the oracle in a stepwise manner, analog circuits are designed to test multiple possible joint-states simultaneously by assigning each a relative weight (e.g., f(000), f(001), …, f(111)). This approach allows the user to learn one or more piece of information after each operation. For example, a non-biological algorithm of the oracle problem58 starts by initializing all values in the joint-state to 0 (i.e., f(ijk) = f(000) = 100%), and then simultaneously represents all possible hidden strings (i.e., f(000) = f(010) = f(100) = f(110) = 25%) (Fig. 6b; steps 1 and 2). Then, to solve the oracle, the user applies an analog operation to learn which digits in the hidden string are matched with the answer signal (i.e., positive selection) and then validates that the remaining digits in the hidden string are not matched with the answer signal (i.e., negative selection) (Fig. 6b; steps 3 and 4, respectively). For example, in the case where the hidden string is [01], the correct joint-state is [01 1]. Therefore, after positive selection, the remaining joint-states [01 1] and [11 1] are equally weighted (i.e., f(011) = f(111) = 50%). After negative selection, the user is able to determine that the correct joint-state with the highest weight is [01 1] (i.e., f(011) = 100%).

Based on this previously described analog algorithm, we next sought to show that proteases can be used to implement and solve the oracle problem. When a protease is in a shared resource environment with two substrates (i.e., blue and red; Fig. 6c), its fractional cleavage scores f(A)and f(B) take on a value between 0% and 100%, which is analogous to relative weights. Therefore, if two proteases are designated to represent possible hidden string values (gray and blue) and one protease (red) to represent the answer signal, this system of three proteases can then represent all eight possible joint-states simultaneously. For example, in the setting where uniform resource sharing is ON for the two hidden string proteases (i.e., gray and blue protease each cleaving substrates A and B equally, or f(A) = f(B) = 50%) and resource sharing for the answer protease is OFF (i.e., red protease; f(A) = 100% and f(B) = 0%), then the weights for all eight possible joint-states (i.e., f(AAA), f(AAB), …, f(BBB)) can be individually calculated by multiplying the fractional cleavage scores of the three proteases (e.g.,., f(AAA) = 25%) (Fig. 6c). These analog weights for the joint-states would always total to 100%, and its values would depend on the extent of resource sharing for each of the three proteases.

Based on the non-biological implementation of the oracle problem58, we designed analog biocircuits using a combination of Uniform (i.e., U1 and U2) and Linker operations to solve the oracle for the particular joint-state f(AB B) (Fig. 7a). All proteases were initialized to cleaving substrate-A (i.e., joint-state f(AAA) = 100%)). Then to represent all hidden strings simultaneously (i.e., answer signal, k = A; f(AAA) = f(ABA) = f(BAA) = f(BBA) = 25%)), we applied U1 Uniform operations to protease pools 1 and 2 to create a state of shared resources within each pool (Fig. 7a; steps 1 and 2). Then, we applied L-operation(s) to reveal which protease pools in the hidden string were matched with the answer signal (i.e., positive selection) by outputting matched proteases to the same state of shared resources (i.e., pool-2 f(B) = 100%, pool-3 f(B) = 100%) (Fig. 7a; step 3). Lastly, we validated that the remaining protease pool (i.e., pool-1) was not matched with the answer signal (i.e., negative selection) by applying three U2 Uniform operations, which reversed pool-1 to a state of non-shared resources cleaving substrate-A (i.e., pool-1 f(A) = 100%) (Fig. 7a; step 4). Experimentally, we found that this protease-based, shared resources solution yielded the correct joint-state with the highest weight (f(ABB) = 80%, n = 3) when compared to the seven other possible joint-states (each other answer <12%, n = 3) (Fig. 7a). To further support these results, we solved the remaining three implementations of the oracle problem (proteases and substrates in Table 1), which were f(AAB), f(BAB), and f(BBB). In each of these three configurations, the protease-based solution outputted the correct joint-state with the highest weight (Fig. 7b).

## Discussion

By interpreting protease activity as carrying digital or analog information, we demonstrated the use of proteases in molecular biocircuits. We used two-valued protease operations carried out by Boolean logic gates to construct an ADC as an autonomous drug delivery biocircuit to clear blood of bacteria across nine orders magnitude in concentration. To construct our biological ADC, we designed biocomparators using peptide-caged liposomes because these materials are well-tolerated and biologically compatible59,60. Our molecular approach is distinct from existing synthetic protein36 and genetic circuit48,61 methods that require protein or organismal engineering to control signaling, including the non-trivial OFF state for proteases which has required insertion strategies20,21,22 artificial autoinhibitors36, or dimerizing leucine zippers48 to control. Parallel to our use of liposome-based particles to control protease activity, liposomes have also been used in past studies such as synthetic minimal cells to control the expression of genetic circuits by liposome fusion49,62. Our approach may be amenable to integration with these genetic approaches, if for example, these circuits were redesigned to input or output (i.e., express) proteases.

In addition to manipulating exclusively two-valued protease operations, we demonstrated the use of proteases in a shared resources environment to represent analog operations. Analog biocircuits24,63 have been demonstrated in systems including gene circuits, DNA strand displacement cascades, and restriction enzymes27,28,29,45. Many of these circuits23,63,64,65 primarily process continuous signals via summing, thresholding, and binning using comparators28 or linear threshold circuits29 (i.e., multi-input comparator). A few studies have used analog biocircuits to represent and operate on multiple states simultaneously by assigning a weight to each state (i.e., fuzzy logic). As an example, a pool of restriction enzymes, FokI, was partitioned between different DNA substrates to represent two output states with a weight (i.e., 0 → 100%) calculated by the relative abundance of each DNA product (i.e., 16 versus 17 nt-long product bands)45. By comparison, we used promiscuous proteases to create a shared resources environment where one protease is partitioned between cleaving two peptide substrates. In the context of the oracle problem, we used three pools of proteases in a state of shared resources to represent eight output states simultaneously. The strategy of representing multiple states simultaneously enables problems that typically require an iterative approach (i.e., each possible answer is tested sequentially) to be solved more efficiently, which has been demonstrated using strand displacement cascades to solve combinatorial math problems15. Looking forward, this strategy may have advantages for implementing biological problems that require a type of iterative approach, such as the decryption of gene circuits66.

## Methods

### Protease cleavage assays

All protease cleavage assays were performed with a BioTek Cytation 5 Imaging Plate Reader, taking fluorescent measurements at 485/528 nm and 540/575 nm (excitation/emission) for read-outs measuring peptide substrates terminated with FITC (Fluorescein isothiocyanate) and 5-TAMRA (5-Carboxytetramethylrhodamine), respectively. Kinetic measurements were taken every minute over the course of 60–120 min at 37 C. West Nile Virus NS3 protease (WNVp) and Tobacco Etch Virus protease (TEVp), along with their substrates, inhibitors and buffers were obtained from Anaspec, Inc. (Fremont, CA). Phospholipase C (PLC), Phosphatidylinositol-Specific (from Bacillus cereus) was purchased from Thermo Fisher Scientific (Waltham, MA). Activity RFU measurements were normalized to time 0 measurement, and as such later time points (after time-0) represent fold change in signal. Granzyme B (GzmB) was purchased from PeproTech, Inc. (Rocky Hill, NJ). Thrombin and Factor XIa were purchased from Haematologic Technologies (Essex, VT). Outer Membrane Protease T (OmpT, Protease 7) was purchased from Lifespan Biosciences (Seattle, WA). C1r was purchased from Millipore Sigma (Burlington, MA). GzmB, Thrombin, Factor XIa, and C1r fluorescent peptide substrates were custom ordered from CPC Scientific (Sunnyvale, CA). OmpT fluorescent peptide substrate was custom ordered from Genscript (Piscataway, NJ). See Tables S1 and S2 for more information regarding proteases, substrates, peptides, and inhibitors.

In all, 10 μL of liposomes (34 mM lipids) loaded with TEVp (1 μg protease/17 mmol liposome) were coincubated with 50 μL TEVp substrate in provided activity buffer (pH 7.5). In all, 2 μL of PLC (100 U/mL) was added to the experimental group, and 2 μL of assay buffer was added to the control group (Fig. 2b).

In all, 10 μL of liposomes (34 mM lipids) loaded with TEVp (1 μg protease/17 mmol liposome), embedded with 10 mol% CPAA and crosslinked at 0.1% efficiency with GzmB substrate were coincubated with 50 μL TEVp substrate in provided activity buffer (pH 7.5). In all, 2 μL PLC (100 U/mL) was added to both the control and experimental group. In all, 2 μL GzmB (0.1 μg/uL) was added only to experimental group (Fig. 2c).

All amounts of protease, substrate, and inhibitor for WNVp and TEVp were added according to instructions from Anaspec WNVp and TEVp activity kit. All conditions incubated with WNVp inhibitor include protease of interest incubated with its primary substrate. GzmB was added at a working concentration of (0.01 mg/mL) to 2 μM of its peptide substrate (Fig. 2d).

All four biocomparator levels (b0 – b3, 50 mM lipids each) were added together (10 μL each), and co-incubated with 13 μL of GzmB solution (concentration varies depending on condition), 2 μL of PLC (100 U/mL), 0.5 μL of WNVp substrate (after diluted 100x according to manufacturer’s instructions), 0.5 μL of TEVp substrate (after diluted 100x according to manufacturer’s instructions), and 4 μL of assay buffer. Biocomparator levels 0–3 are referenced by peptide cage crosslinking efficiencies of 0%, 0.01%, 1%, and 100%, respectively. Plotted values are taken at minute 30 and normalized to starting values (time 0, or equivalently, the no protease control). Unpaired, one-way t-tests (n = 4) were performed between the condition with GzmB and the negative control (no GzmB) for each respective output (i.e., p0 or p1) (Fig. 2e).

For recombinant OmpT condition, 2 μL of OmpT (0.5 mg/mL) was added to 18 μL of 2 μM OmpT substrate. For E. coli condition, 2 μL of E. coli (109 CFU/mL) was added to 18 μL of 2 μM OmpT substrate. In all, 2 μL of DI H2O was added to the negative control, along with 18 μL of OmpT substrate (Fig. 3b).

For Fig. 4a, the protease Plasmin (250 nM) was incubated with substrate-A (5FAM-KSVARTLLVK-(LysCPQ2)-C; concentration = 0.24 μM) in PBS. For Fig. 4 b, the protease Plasmin (250 nM) was incubated with substrate-A (5FAM-KSVARTLLVK-(LysCPQ2)-C; concentration = 0.24 μM) as well as substrate-B (DABCYL-GPAALKAG-EDANS-R; concentration = 0.2 μM) simultaneously in PBS. Experiments were read on Plate Reader at 37 C with plate sealer and plastic plate cover (Fig. 4a, b).

For Fig. 4c panel i, the protease Thrombin (250 nM) was incubated with substrate-A (5FAM-KTTGGRIYGG-(LysCPQ2)-C; concentration = 0.64 μM) in PBS. For panel ii, the protease Thrombin (250 nM) was incubated with substrate-A (5FAM-KTTGGRIYGG-(LysCPQ2)-C; concentration = 0.64 μM) as well as substrate-B (DABCYL-GPLGL-(DAP)-ARG-EDANS-R; concentration = 6.75 μM) simultaneously in PBS. For panel iii, the protease Thrombin (250 nM) was incubated with substrate-A (5FAM-KTTGGRIYGG-(LysCPQ2)-C; concentration = 1.28 μM) as well as substrate-B (DABCYL-GPLGL-(DAP)-ARG-EDANS-R; concentration = 6.75 μM) simultaneously in PBS. Experiments were read on Plate Reader at 37 C with plate sealer and plastic plate cover (Fig. 4c).

For Fig. 5b panel i, the protease MMP7 (250 nM) was incubated with substrate-A (5FAM-KYLGRSYKV-(LysCPQ2)-C; concentration = 2.53 μM) as well as substrate-B (DABCYL-GPAALKAG-EDANS-R; concentration = 29.90 μM) simultaneously in PBS. The protease Plasmin (250 nM) was incubated with substrate-A (5FAM-KSVARTLLVK-(LysCPQ2)-C; concentration = 0.24 μM) in PBS. For panel II, the protease MMP7 (250 nM) was incubated with substrate-A (5FAM-KYLGRSYKV-(LysCPQ2)-C; concentration = 2.53 μM) as well as substrate-B (DABCYL-GPAALKAG-EDANS-R; concentration = 149.50 μM) simultaneously in PBS. The protease Plasmin (250 nM) was incubated with substrate-A (5FAM-KSVARTLLVK-(LysCPQ2)-C; concentration = 0.24 μM) as well as substrate-B (DABCYL-GPAALKAG-EDANS-R; concentration = 74.75 μM) simultaneously in PBS (Fig. 5b).

For Fig. 7a, Oracle f(AB B), graph (1), the protease Thrombin (250 nM) was incubated with substrate-A (5FAM-KTTGGRIYGG-(LysCPQ2)-C; concentration = 0.64 μM) in PBS. The protease MMP7 (250 nM) was incubated with substrate-A (5FAM-KYLGRSYKV-(LysCPQ2)-C; concentration = 2.53 μM) in PBS. The protease Plasmin (250 nM) was incubated with substrate-A (5FAM-KSVARTLLVK-(LysCPQ2)-C; concentration = 0.24 μM) in PBSg (Fig. 7a, b).

For Fig. 7a, Oracle f(AB B), graph (2), the protease Thrombin (250 nM) was incubated with substrate-A (5FAM-KTTGGRIYGG-(LysCPQ2)-C; concentration = 0.64 μM) as well as substrate-B (DABCYL-GPLGL-(DAP)-ARG-EDANS-R; concentration = 6.75 μM) simultaneously in PBS. The protease MMP7 (250 nM) was incubated with substrate-A (5FAM-KYLGRSYKV-(LysCPQ2)-C; concentration = 2.53 μM) as well as substrate-B (DABCYL-GPAALKAG-EDANS-R; concentration = 29.90 μM) simultaneously in PBS. The protease Plasmin (250 nM) was incubated with substrate-A (5FAM-KSVARTLLVK-(LysCPQ2)-C; concentration = 0.24 μM) in PBS.

For Fig. 7a, Oracle f(AB B), graph (3), the protease Thrombin (250 nM) was incubated with substrate-A (5FAM-KTTGGRIYGG-(LysCPQ2)-C; concentration = 0.64 μM) as well as substrate-B (DABCYL-GPLGL-(DAP)-ARG-EDANS-R; concentration = 6.75 μM), plus an additional substrate-B for the Linker operation (DABCYL-GPAALKAG-EDANS-R; concentration = 29.90 μM), simultaneously in PBS. The protease MMP7 (250 nM) was incubated with substrate-A (5FAM-KYLGRSYKV-(LysCPQ2)-C; concentration = 2.53 μM) as well as substrate-B (DABCYL-GPAALKAG-EDANS-R; concentration = 29.90 μM) plus an additional substrate-B for the Linker operation (DABCYL-GPAALKAG-EDANS-R; concentration = 119.60 μM), simultaneously in PBS. The protease Plasmin (250 nM) was incubated with substrate-A (5FAM-KSVARTLLVK-(LysCPQ2)-C; concentration = 0.24 μM) plus an additional substrate-B for the Linker operation (DABCYL-GPAALKAG-EDANS-R; concentration = 74.75 μM), in PBS.

For Fig. 7a, Oracle f(AB B), graph (4), the protease Thrombin (250 nM) was incubated with substrate-A (5FAM-KTTGGRIYGG-(LysCPQ2)-C; concentration = 1.28 μM) as well as substrate-B (DABCYL-GPLGL-(DAP)-ARG-EDANS-R; concentration = 6.75 μM), plus an additional substrate-B for the Linker operation (DABCYL-GPAALKAG-EDANS-R; concentration = 29.90 μM), simultaneously in PBS. The protease MMP7 (250 nM) was incubated with substrate-A (5FAM-KYLGRSYKV-(LysCPQ2)-C; concentration = 5.07 μM) as well as substrate-B (DABCYL-GPAALKAG-EDANS-R; concentration = 29.90 μM) plus an additional substrate-B for the Linker operation (DABCYL-GPAALKAG-EDANS-R; concentration = 119.60 μM), simultaneously in PBS. The protease Plasmin (250 nM) was incubated with substrate-A (5FAM-KSVARTLLVK-(LysCPQ2)-C; concentration = 0.48 μM) plus an additional substrate-B for the Linker operation (DABCYL-GPAALKAG-EDANS-R; concentration = 74.75 μM), in PBS.

For Fig. 7b, Oracle f(AA B) (i.e., graph 4), the protease Thrombin (250 nM) was incubated with substrate-A (5FAM-KTTGGRIYGG-(LysCPQ2)-C; concentration = 1.28 μM) as well as substrate-B (DABCYL-GPLGL-(DAP)-ARG-EDANS-R; concentration = 6.75 μM), plus an additional substrate-B for the Linker operation (DABCYL-GPAALKAG-EDANS-R; concentration = 29.90 μM), simultaneously in PBS. The protease Complement protease C1s (250 nM) was incubated with substrate-A (5FAM- KYLGRSYKV -(LysCPQ2)-C; concentration = 0.24 μM) as well as substrate-B (DABCYL-GPAALKAG-EDANS-R; concentration = 29.90 μM) plus an additional substrate-B for the Linker operation (DABCYL-GPAALKAG-EDANS-R; concentration = 29.90 μM), simultaneously in PBS. The protease Plasmin (250 nM) was incubated with substrate-A (5FAM-KSVARTLLVK-(LysCPQ2)-C; concentration = 0.48 μM) plus an additional substrate-B for the Linker operation (DABCYL-GPAALKAG-EDANS-R; concentration = 74.75 μM), in PBS.

For Fig. 7b, Oracle f(BA B) (i.e., graph 4), the protease MMP7 (250 nM) was incubated with substrate-A (5FAM-KYLGRSYKV-(LysCPQ2)-C; concentration = 5.07 μM) as well as substrate-B (DABCYL-GPAALKAG-EDANS-R; concentration = 29.90 μM) plus an additional substrate-B for the Linker operation (DABCYL-GPAALKAG-EDANS-R; concentration = 119.60 μM), simultaneously in PBS. The protease Thrombin (250 nM) was incubated with substrate-A (5FAM-KTTGGRIYGG-(LysCPQ2)-C; concentration = 1.28 μM) as well as substrate-B (DABCYL-GPLGL-(DAP)-ARG-EDANS-R; concentration = 6.75 μM), plus an additional substrate-B for the Linker operation (DABCYL-GPAALKAG-EDANS-R; concentration = 29.90 μM), simultaneously in PBS. The protease Plasmin (250 nM) was incubated with substrate-A (5FAM-KSVARTLLVK-(LysCPQ2)-C; concentration = 0.48 μM) plus an additional substrate-B for the Linker operation (DABCYL-GPAALKAG-EDANS-R; concentration = 74.75 μM), in PBS.

For Fig. 7b, Oracle f(BB B) (i.e., graph 4), the protease MMP7 (250 nM) was incubated with substrate-A (5FAM-KYLGRSYKV-(LysCPQ2)-C; concentration = 5.07 μM) as well as substrate-B (DABCYL-GPAALKAG-EDANS-R; concentration = 29.90 μM) plus an additional substrate-B for the Linker operation (DABCYL-GPAALKAG-EDANS-R; concentration = 119.60 μM), simultaneously in PBS. The protease Cathepsin G (250 nM) was incubated with substrate-A (5FAM-KSVARTLLVK-(LysCPQ2)-C; concentration = 0.05 μM) as well as substrate-B (DABCYL-GPAALKAG-EDANS-R; concentration = 29.90 μM), plus an additional substrate-B for the Linker operation (DABCYL-GPAALKAG-EDANS-R; concentration = 29.90 μM), simultaneously in PBS. The protease Plasmin (250 nM) was incubated with substrate-A (5FAM-KSVARTLLVK-(LysCPQ2)-C; concentration = 0.48 μM) plus an additional substrate-B for the Linker operation (DABCYL-GPAALKAG-EDANS-R; concentration = 74.75 μM), in PBS.

### Liposome synthesis and characterization

Liposome synthesis kit, PIPES buffer, EDC*MeI, and spin filters (100 kDa m.w.c.o. filters) were purchased from Millipore Sigma (Burlington, MA). Cholesterol-anchored Polyacrylic Acid (4400 g/mol, 30–40 COOH groups/molecule, structure in Fig. S2a) was custom ordered from Nanocs (Boston, MA). Float-a-lyzer dialysis tubes (100 kDa m.w.c.o., 1 mL) were purchased from Spectrum Labs (Rancho Dominguez, CA). Synthesis protocol is adapted from the methods used by Basel et. al. Liposomes were loaded with respective protease inhibitor cocktail amounts, and concentration was estimated via absorbance. Standard curve for estimating concentration of liposomes was used by correlating absorbance of liposome solution at 230 nm with known standard concentrations. CPAA was vortexed in warm water (<10 mg/mL) and volume was added such that there was 10 mol% CPAA relative to the molarity of lipids in the liposome solution. Solution was incubated for 1 h at room temperature, or overnight at 4 C. Excess polymer and materials were removed via centrifugation (spin filters, 3–5 times at 4700 × g for 10 min) or float-a-lyzer membranes (4 °C in spinning water overnight). EDC*MeI was dissolved into 10 mM PIPES buffer and volume was added such that EDC*MeI:CPAA ratio was 4:1. Solution was incubated for 20 min at room temperature. Excess EDC was filtered out via centrifugation or dialysis tubes. Peptide crosslinker was added at desired molar ratio and incubated for 1 hour at room temperature or overnight at 4 C. Excess peptide was filtered via centrifugation or dialysis tubes. Change in liposome hydrodynamic diameter was measured via DLS on a Zetasizer Nano ZS, Malvern Panalytical (Netherlands). Volumes loaded into biocomparators include concentrations of proteases and inhibitors as follows: b0 = empty; b1 = 20 μL WNVp (0.1 mg/mL) + 80 μL DI H2O; b2 = 50 μL WNV inhibitor (1 μM) + 50 μL TEVp (0.04 mg/mL); b3 = 50 μL WNVp (0.1 mg/mL) + 50 μL TEVp (0.08 mg/mL).

### Bacterial cytotoxicity and human red blood cell hemolysis assays

Bacterial culture and cytotoxicity measurement. DH5α Escherichia coli were a gift from Todd Sulchek’s BioMEMS lab at Georgia Tech. E. coli were cultured in LB broth (Lennox) at 37 °C and plated on LB agar (Lennox) plates. LB broth was purchased from Millipore Sigma (Burlington, MA) and LB agar was purchased from Invitrogen (Carlsbad, CA). AMP and locked AMP were custom ordered from Genscript (Piscataway, NJ). See Table S1 for more information. Bacteria were grown to a concentration of 109 CFU/mL before being used for experiments. Concentration was estimated by measuring the OD600 of the bacterial suspension, where an OD600 of 1.000 corresponds to a concentration of 8 × 108 CFU/mL. Bacterial cell viability was measured by making eight 10-fold serial dilutions, and plating three 10 μL spots on an LB agar plate. Plates were incubated overnight at 37 °C, and CFUs were counted. Untreated bacteria CFU counts served as control for 0% cytotoxicity, and bacteria + IPA (or 0 countable CFUs) served as control for 100% cytotoxicity.

RBC collection and hemolysis measurement. Healthy blood donors had abstained from aspirin in the last two weeks, and written informed consent was obtained according to GT IRB H15258. Blood was drawn by median cubital venipuncture into sodium citrate (3.2%). The sample was subsequently centrifuged at 150 × g for 15 min, and the resulting platelet rich plasma was discarded. Red blood cells were then washed three times with phosphate buffered saline (PBS). For each wash, 12 mL of PBS were added, the sample was centrifuged at 200 × g for 10 min, and the supernatant was discarded. Hemolysis was estimated by spinning down experimental RBC samples and measuring the absorbance of the supernatant at 450 nm. Absorbance values corresponding to 100% hemolysis came from incubating RBCs with 0.1% Tween-20. Absorbances corresponding to 0% hemolysis came from untreated RBCs.

For bacterial cytotoxicity measurements, 25 μL of antimicrobial peptide (AMP) was added, pertaining to 7 concentrations ranging between 7.6 nM and 7.6 mM. In all, 20 μL of bacteria (107 CFU/mL) were added, and the sample was filled to 200 μL with LB broth in PCR tubes. Sample tubes were taped on a plate shaker (250 RPM) incubating at 37 C for 8 h. For RBC hemolysis measurements, the same assay was performed, but used 20 μL of donor RBCs instead of bacteria solution (Fig. 3c).

For bacteria only condition, 5 μL of bacteria (109 CFU/mL) were added to 95 μL LB broth. For bacteria + AMP p1, 58 μL of AMP p1 (1.7 mM) were added to 5 μL of bacteria (109 CFU/mL), with the solution being filled to 100 μL with LB broth. For bacteria + protease + locked AMP p1, 20 μL of TEVp (4 μg/mL) and 58 μL of AMP p1 (1.7 mM) were added to 5 μL of bacteria (109 CFU/mL), with the solution being filled to 100 μL with LB broth. Samples in PCR tubes were taped to a plate shaker (250 RPM) incubating at 37 C for 1 h. Serial dilutions and plating were then performed to measure viable bacteria concentrations (Fig. 3d).

Each condition includes 20 μL of the bioprogram (2 μL of PLC, 6 μL D1, 6 μL D2, 6 μL D3), 20 μL of bacteria, 10 μL of RBCs, 24 μL of locked peptide drug (9 μL of 1.7 mM AMP p1 and 15 μL of 0.53 mM AMP p0), and 126 μL PBS. The concentration of bacteria, and the presence of each biocomparator, depends on the experimental condition (Fig. 3e). Samples in PCR tubes were taped to a plate shaker (250 RPM) incubating at 37 C for 8 h, followed by dilutions/plating to estimate bacterial cytotoxicity. The remainder of the sample was spun down by centrifugation and used to estimate hemolysis (Fig. 3e).

### Statistics and reproducibility

Statistical analysis was performed using statistical packages included in GraphPad Prism 6. To assess the significance of increase in signal due to protease cleavage, we used a two-way ANOVA (without repeated measures) followed by Sidak’s multiple comparisons test. To assess the accuracy of assigning the binary value 0 or 1 to the digits p0 and p1 as seen in Fig. 2e, one-way unpaired t-tests were performed between the condition with GzmB and the negative control (no GzmB) for each respective output (i.e., p0 or p1) (Fig. 2e). A one-way ANOVA followed by Dunnett’s multiple comparisons test was used to compare experimental means to cells only control in Fig. 3d. Experiments in Figs. 1b, c, 2b, c, 3b, 4, and 5, were reproduced three times total with similar results. Experiments in Figs. 2e, 3c–e, and 7b were reproduced two times total with similar results. Experiments in Figs. 2d and 7a were performed once.

### Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

## Data availability

The authors declare that the data supporting the findings of this study are available within the paper and its supplementary information files. Any other relevant data are available upon reasonable request. Source data are provided with this paper.

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## Acknowledgements

This work was funded by an NIH Director’s New Innovator Award (Award No. DP2HD091793) as well as an R01 from the NCI (GR10003709). B.A.H is supported by the NSF GRFP, National Institutes of Health GT BioMAT Training Grant under Award Number 5T32EB006343 and the Georgia Tech President’s Fellowship. This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-1650044 (B.A.H.). G.A.K. holds a Career Award at the Scientific Interface from the Burroughs Welcome Fund. We acknowledge Dr. David Myers (Georgia Tech and Emory), Dr. Peng Qiu (Georgia Tech), Anirudh Sivakumar (Georgia Tech), and Shreyas N. Dahotre (Georgia Tech and Emory) for their helpful discussions during the preparation of the paper. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

## Author information

Authors

### Contributions

B.A.H. and G.A.K. designed research; B.A.H. performed research; B.A.H. and G.A.K. analyzed data; and B.A.H. and G.A.K. wrote the paper.

### Corresponding author

Correspondence to Gabriel A. Kwong.

## Ethics declarations

### Competing interests

G.A.K. is co-founder of and serves as consultant to Glympse Bio, which is developing products related to the research described in this paper. This study could affect his personal financial status. The terms of this arrangement have been reviewed and approved by Georgia Tech in accordance with its conflict of interest policies. B.A.H. and G.A.K. are listed as inventors on a patent application pertaining to the results of the paper. The patent applicant is the Georgia Tech Research Corporation. The names of the inventors are Brandon Holt and Gabriel Kwong. The application number is PCT/US19/051833. The patent is currently pending/published (publication no. WO 2020/061257). The biological ADC and the analog protease circuits are covered in the patent.

Peer review information Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work.

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Holt, B.A., Kwong, G.A. Protease circuits for processing biological information. Nat Commun 11, 5021 (2020). https://doi.org/10.1038/s41467-020-18840-8

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• ### Synthetic biomarkers: a twenty-first century path to early cancer detection

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• , Sharmistha Ghosh
• , Lena Gamboa
• , Christos Patriotis
• , Sudhir Srivastava
•  & Sangeeta N. Bhatia

Nature Reviews Cancer (2021)